ML_Classification_Support_Vector_Machines

Use scikit-learn to Support Vector Machine to classify

Table of contents

• Load the Cancer data
• Data pre-processing and selection
• Modeling (SVM)
• Evaluation
• Practice

Objectives

We will use SVM (Support Vector Machines) to build and train a model using human cell records, and classify cells to whether the samples are benign or malignant.

About SVM

SVM works by mapping data to a high-dimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable. A separator between the categories is found, then the data is transformed in such a way that the separator could be drawn as a hyperplane. Following this, characteristics of new data can be used to predict the group to which a new record should belong.

Downloading the Data

To download the data, we will download it from IBM Object Storage. 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/cell_samples.csv'